Hi! I enrolled in the DeepLearning Specialization, and I’m on week two. I’m finding it informative and will finish the course to get as much exposure as I can, but I would like to find another course that is more geared towards regression analysis vs classification as the main focus of the course. For example where the Loss function is the Mean Squared Error (or other appropriate function) rather than the Cross Entropy function. As a thought experiment and teaching the fundamentals this course is somewhat useful, but since it completely overlooks my specialization (as a physicist and director of research for a company that uses engineering functions), it is not as didactic as I would like. That being said are there other treatments of deep learning for regression analysis out there that any of you know of? On Coursera or other… At the moment ChatGPT is proving to be my best teacher….

The Machine Learning Specialization has more examples of linear regression. Typically students will attend that before moving on to DLS.

And you can likely find a lot of them for practice in the datasets area of Kaggle.

Thanks for your input @TMosh. Though it is much appreciated I think I have not correctly represented my need: I have spent the last 6 months developing a machine learning algorithm for my regression analysis and it has become apparent that I need at least 2 hidden layers, and the long-term scope of this project will potentially have many more layers than that. As well, my model is not linear, and although I am currently employing R-Squared as a placeholder metric, I will be needing to use more advanced metrics for evaluating goodness of fit for nonlinear models. I am specifically looking for a Deep Learning treatment of nonlinear regression analysis with multiple parameters, and for which multiple mathematical models will be used in sequence for fitting past data and generating forecasts for future data. I’m wondering if and where I might find such a treatment? Or if I need to invent it myself… My current program is in C#, but I can program in Python. C# is preferable though for several reasons, so I am also looking for a treatment of NNs using C#. Kind regards, Alix

Thanks for the clarification.

A NN with a “regression” output is just a regular output layer with no activation function. You can have multiple outputs if necessary.

“linear regression” doesn’t refer to the shape of the model - it refers to using the linear combination of the features and weights.